Two-layered compact star in biquadratic spatial metric with distinct core-shell equations of state Shweta Saklany, Neeraj Pant, Brajesh Pandey Physics Letters Section B Nuclear Elementary Particle and High Energy Physics, 2025 We develop a smoothly matched and singularity-free model for a two-layered compact star, comprising an MIT bag model type dense core surrounded by a shell of Bose-Einstein condensate. Using a biquadratic spatial metric as the coherent background, the temporal metric potential for each layer is obtained by solving the Einstein field equations. This approach provides a comprehensive understanding of the pressure-density distribution and thermodynamic behavior within the star, offering critical insights into its stability, mass-radius relationship, and overall physical feasibility. A key finding of this study is the existence of a stable core-shell structure, where the two distinct regions are smoothly connected at the junction Image 1. Another distinctive feature is the formation of a uniform flow zone close to core-shell junction, where the tangential sound speed remains nearly constant vts,c(r)≈0.374.
Three-layered compact star in modified Buchdahl-I spatial metric with distinct equations of state Shweta Saklany, Neeraj Pant, Brajesh Pandey Physics Letters Section B Nuclear Elementary Particle and High Energy Physics, 2023 We present a singularity free and smoothly connected complete solution for a three-layered compact star with deconfined quark matter core surrounded by coherent quantum fluid of a Bose-Einstein condensate, all enclosed under a thin envelope of a repulsive neutron-Coulomb fluid. A modified form of Buchdahl-I type spatial metric is used as a seed to obtain the explicit temporal metric potential for each layer using Einstein field equation. The process allows us to obtain insights into the pressure-density profile and associated thermodynamic properties within stellar interior, shedding light on its structural stability, mass-radius relation and physical plausibility. One of the key finding of our work is the close similarity between the M–R curve of our three-phase model star Vela X-1 and that of a strange quark star model.
Research Network Analysis and Machine Learning Modeling on Heusler Alloys Aparna Ashok, , Anjana Desai, Rajesh Mahadeva, Shashikant P. Patole, Brajesh Pandey, Neeru Bhagat, , , , , and Engineered Science, 2023 Heusler alloys are an incredible class of inter-metallic materials with different compositions and over 1500 members. Though discovered a century back, they are an active area of physics and material science research. Novel properties and potential fields of applications materialize constantly. Even the alloy system is extensively investigated owing to its shape memory behavior and prospective relevance in the development of actuator devices, where strains are controlled by applying an external magnetic field. Heusler alloys are currently the material of interest due to their properties leading to their use as shape memory alloys and topological insulators. Hence, predicting and determining their composition and structure is imperative before synthesis. Utilizing the conventional method in determining the possible changes in the properties and the structure of the proposed compositions is tedious and time-consuming. In the current consumerism-driven environment, we require a faster method to predict the structure of the proposed alloy or compound or other parameters for the desired application. Once the prediction is made, it must be tested experimentally by synthesizing the material and characterizing its behavior. This analysis is focusing on network analysis with a supervised machine learning approach to study the properties of Heusler alloys with their application as shape memory alloys.
Meta-Analysis of Cytotoxicity Studies Using Machine Learning Models on Physical Properties of Plant Extract-Derived Silver Nanoparticles Anjana Desai, Aparna Ashok, Zehra Edis, Samir Bloukh, Mayur Gaikwad, Rajendra Patil, Brajesh Pandey, Neeru Bhagat International Journal of Molecular Sciences, 2023 Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules’ mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R2. The obtained high value of R2 and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments.
Ab initio study of Y3AlZ (Z = B, C, N, O) A. S. Ghule, S. S. Ghule, C. S. Garde, B. Pandey, S. Ramakrishnan, S. Singh, A. K. Rajarajan Aip Conference Proceedings, 2019
